Lessons Detailing Exploratory Drought and Flood Analysis
Joshua Brinks
Research Scientist, ISciences, LLC
2024-01-09
Presentation Outline
Module Goals
Learning objectives
Open Science Components
Technical details
Proposed analyses and outputs
Lesson Goals
The objective of this lesson is to provide content for learning water resource issues through data exploration and analysis.
We are introducing 3 datasets for this module
MODIS NRT Flood Data
NYC Lead Data
WSIM-GLDAS
Open Science Components
Like all other components of the SCHOOL project, the water resource modules are entirely open source.
Open source computing (R, Rstudio, VS Code, QGIS, etc.)
Open source datasets; freely available and well documented
Open source development
Open source “results” on GitHub Pages
Narrative Objectives
We are currently in the early development phases and need to adjust the narrative voice between 3 learning/content components.
Water resources
Technical data
Technical coding
Water Resource Narrative
These include introducing the domain knowledge and human narrative that can be derived from these datasets.
Understanding the socioeconomic and environmental impacts of droughts and floods.
Exploring drought and flooding events in areas of interest to you and learning about the impacts to local water supplies, agriculture, recreation, and tourism.
Basic background on the water cycle.
Technical Data Narrative
What does water resource data “look” like?
Where do you find it and how do you get it?
Coding Narrative
Are we interested at all in teaching people how to code? Unlikely but then how do you address all the code.
# generate a vector of dates for subsettingkeeps<-seq(lubridate::ymd("2000-01-01"), lubridate::ymd("2014-12-01"), by ="month")# filter using that vectorwsim_gldas_anoms <- dplyr::filter(wsim_gldas_anoms, time %in% keeps)# verify the time dimension was properly subsettedprint(wsim_gldas_anoms)# do a visual check with the first 6 time-stepswsim_gldas_anoms |> dplyr::slice(index =1:6, along ="time") |>plot(key.pos =1)
WSIM-GLDAS
WSIM-GLDAS is an open source dataset that characterizes surpluses and deficits of freshwater and the parameters determining these anomalies.
Globally rasterized dataset issued monthly for 1948-2014
Composite surplus/deficit anomalies
Multitude of additional metrics and integration periods
Whatever the chosen narrative voice and content, we hope to bring greater understanding for each module through visualizations and analysis. The WSIM-GLDAS water resource modules will achieve this by creating:
National and regional 12 month integration composite surplus/deficit maps
Time series illustrations of point locations
Population exposure time series figures and tables
Composite Surplus and Deficit Maps
Twelve month integration maps illustrate the observed drought or flooding of an area relative to a long term baseline period.
Composite Surplus and Deficit Maps (cont.)
Now you can zoom in on an area of interest with a monthly time series.
Location of Interest
Point location time series figures illustrate long term trends for a single location on a month to month basis.
Population Exposure
Population exposure plots and tables help illustrate the sociological impacts of droughts and floods.
Current Drafts
Although the module and lesson structure is yet to be finalized, we have some rough drafts in place that demonstrate the baseline technical workflow and present a template for dev assistants.
